DC 1: AI-guided design of preQ1-II and SAM-III riboswitch ligands

PhD project description:

The chemical structures of known ligands and discovered hits within this project will serve as input for AI-guided compound design. We will utilize ReInvent to optimize the identified ligands, employing active learning, docking and free energy calculations. To steer the AI toward desired chemical structures possessing advantageous physico-chemical and biological properties, we will closely collaborate within the project to develop relevant machine learning models (QSA/PR). Novel compounds and their proposed binding modes will undergo further investigation via MD simulations. Evaluation of feasibility and the development of new MD protocols will involve examining the stability of known riboswitches. Subsequently, the stability of the proposed RNA-compound complex will be assessed and specifically refining the binding site and binding mode for compound design.

Research environment:

The main goal of the research programme is to develop fundamental knowledge on how to discover and design drug-like, potent, selective, and functional RNA ligands based on the 3D structure of the target and to investigate the antimicrobial activity of the developed compounds, in particular their potential to reshape the gut microbiome.
To achieve these goals, the following objectives have been set:

• Elucidate structural features, including the dynamics, of the investigated targets with and without bound ligands.
• Use the obtained structural information to design and synthesise selective and potent ligands, including covalent ligands and the first BacRIBOTACs.
• Characterized ligands using in vitro, in cellulo and in vivo assays, establishing structure-activity relationships (SAR), elucidating the driving forces for potent and selective RNA binding, and evaluate the potential of the compounds to act as antimicrobials and reshape the gut microbiome.

Skills/qualifications:

We are seeking an engaged candidate with a Master in computational chemistry, chemistry, biochemistry, pharmacology, or a related field with a strong computational background. The candidate is expected to have knowledge in molecular simulations, an interest in machine learning and displaying a high degree of independence. We will concentrate on the following requirements and qualifications:

• Good knowledge of computational chemistry.
• Experience in structure-based and ligand-based design techniques and knowledge in scientific computing, and programming skills (e.g., Python, Perl, C, C++, Java, R) is a plus, not a must.
• Specific knowledge in molecular dynamics with a track record of applying these techniques to a relevant problem in the field is favourable.
• Strong written and verbal communication skills and ability to work well in multidisciplinary project teams.
• Excellent time management skills, forward planning and delivery focus.

Organized research training (PhD program): The candidate must take part in the University of Bergen approved PhD programme leading to the degree within a time limit of 3 years. You must have admission to the organized research training (PhD program) at the Faculty in order to qualify for the position. Application for admission to the PhD programme, including a project plan outline for the training plan must be submitted no later than three months after the date of commencement.

Benefits/salary/social security:

Please adress questions on benefits, salary and soscial security to Christian Tyrchan.

For more information about the position, please contact ChristianTyrchan.